Overview

Dataset statistics

Number of variables20
Number of observations8827
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory153.0 B

Variable types

Text3
Numeric4
Categorical11
DateTime1
Boolean1

Alerts

card_number is highly overall correlated with customer_segment and 3 other fieldsHigh correlation
customer_segment is highly overall correlated with card_numberHigh correlation
card_type is highly overall correlated with card_numberHigh correlation
customer_location is highly overall correlated with card_number and 1 other fieldsHigh correlation
trans_loc is highly overall correlated with card_number and 1 other fieldsHigh correlation
trans_id has unique valuesUnique
trans_approval_code has unique valuesUnique

Reproduction

Analysis started2023-09-09 00:49:34.707259
Analysis finished2023-09-09 00:50:40.987642
Duration1 minute and 6.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct498
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
2023-09-09T06:20:42.009316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length13.243004
Min length8

Characters and Unicode

Total characters116896
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndrea Hess
2nd rowAndrea Hess
3rd rowAndrea Hess
4th rowAndrea Hess
5th rowAndrea Hess
ValueCountFrequency (%)
michael 215
 
1.2%
williams 205
 
1.1%
jones 192
 
1.1%
smith 182
 
1.0%
johnson 168
 
0.9%
john 160
 
0.9%
brown 160
 
0.9%
ashley 145
 
0.8%
melissa 143
 
0.8%
taylor 124
 
0.7%
Other values (545) 16306
90.6%
2023-09-09T06:20:45.473211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10934
 
9.4%
a 10314
 
8.8%
9173
 
7.8%
n 9168
 
7.8%
r 8467
 
7.2%
i 6956
 
6.0%
o 6283
 
5.4%
l 6120
 
5.2%
s 5582
 
4.8%
h 4127
 
3.5%
Other values (41) 39772
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 89304
76.4%
Uppercase Letter 18197
 
15.6%
Space Separator 9173
 
7.8%
Other Punctuation 222
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10934
12.2%
a 10314
11.5%
n 9168
10.3%
r 8467
9.5%
i 6956
 
7.8%
o 6283
 
7.0%
l 6120
 
6.9%
s 5582
 
6.3%
h 4127
 
4.6%
t 3720
 
4.2%
Other values (16) 17633
19.7%
Uppercase Letter
ValueCountFrequency (%)
M 2022
 
11.1%
J 1820
 
10.0%
C 1424
 
7.8%
S 1247
 
6.9%
A 1146
 
6.3%
D 1061
 
5.8%
B 1059
 
5.8%
W 1054
 
5.8%
K 956
 
5.3%
R 950
 
5.2%
Other values (13) 5458
30.0%
Space Separator
ValueCountFrequency (%)
9173
100.0%
Other Punctuation
ValueCountFrequency (%)
. 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107501
92.0%
Common 9395
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10934
 
10.2%
a 10314
 
9.6%
n 9168
 
8.5%
r 8467
 
7.9%
i 6956
 
6.5%
o 6283
 
5.8%
l 6120
 
5.7%
s 5582
 
5.2%
h 4127
 
3.8%
t 3720
 
3.5%
Other values (39) 35830
33.3%
Common
ValueCountFrequency (%)
9173
97.6%
. 222
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10934
 
9.4%
a 10314
 
8.8%
9173
 
7.8%
n 9168
 
7.8%
r 8467
 
7.2%
i 6956
 
6.0%
o 6283
 
5.4%
l 6120
 
5.2%
s 5582
 
4.8%
h 4127
 
3.5%
Other values (41) 39772
34.0%

card_number
Real number (ℝ)

HIGH CORRELATION 

Distinct500
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0232185 × 1011
Minimum6.8775423 × 108
Maximum9.9830793 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.1 KiB
2023-09-09T06:20:46.410910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.8775423 × 108
5-th percentile5.6516319 × 1010
Q12.5897226 × 1011
median4.9406557 × 1011
Q37.3950696 × 1011
95-th percentile9.4976373 × 1011
Maximum9.9830793 × 1011
Range9.9762018 × 1011
Interquartile range (IQR)4.805347 × 1011

Descriptive statistics

Standard deviation2.8029156 × 1011
Coefficient of variation (CV)0.55799198
Kurtosis-1.1257441
Mean5.0232185 × 1011
Median Absolute Deviation (MAD)2.3750417 × 1011
Skewness0.027604481
Sum4.433995 × 1015
Variance7.8563361 × 1022
MonotonicityNot monotonic
2023-09-09T06:20:47.184663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.648952136 × 101124
 
0.3%
1.318558329 × 101124
 
0.3%
6.20106724 × 101124
 
0.3%
1.254398601 × 101124
 
0.3%
4.072110048 × 101124
 
0.3%
4.215858386 × 101124
 
0.3%
9.526770655 × 101124
 
0.3%
4.321999211 × 101124
 
0.3%
7.168650139 × 101124
 
0.3%
1.491716627 × 101124
 
0.3%
Other values (490) 8587
97.3%
ValueCountFrequency (%)
687754227 14
0.2%
1660512712 18
0.2%
4440579128 17
0.2%
6927126924 14
0.2%
7390485495 12
0.1%
1.31270111 × 101015
0.2%
1.588174266 × 101022
0.2%
1.690193362 × 101020
0.2%
1.875705705 × 101014
0.2%
2.194776567 × 101021
0.2%
ValueCountFrequency (%)
9.98307935 × 101122
0.2%
9.975805694 × 101121
0.2%
9.95990208 × 101123
0.3%
9.949190434 × 101115
0.2%
9.94746664 × 101118
0.2%
9.92545598 × 101115
0.2%
9.893812544 × 101115
0.2%
9.876034547 × 101115
0.2%
9.854507522 × 101112
0.1%
9.854390367 × 101112
0.1%

customer_age
Real number (ℝ)

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.598505
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.1 KiB
2023-09-09T06:20:47.918429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median43
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.1068
Coefficient of variation (CV)0.34649812
Kurtosis-1.1938139
Mean43.598505
Median Absolute Deviation (MAD)13
Skewness-0.024435269
Sum384844
Variance228.2154
MonotonicityNot monotonic
2023-09-09T06:20:48.642197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
36 394
 
4.5%
60 371
 
4.2%
47 331
 
3.7%
39 325
 
3.7%
43 321
 
3.6%
51 318
 
3.6%
26 315
 
3.6%
67 294
 
3.3%
50 291
 
3.3%
29 283
 
3.2%
Other values (38) 5584
63.3%
ValueCountFrequency (%)
18 239
2.7%
19 143
1.6%
20 157
1.8%
21 190
2.2%
22 211
2.4%
23 154
1.7%
25 192
2.2%
26 315
3.6%
27 108
 
1.2%
28 149
1.7%
ValueCountFrequency (%)
69 237
2.7%
68 14
 
0.2%
67 294
3.3%
66 214
2.4%
65 38
 
0.4%
64 187
2.1%
63 264
3.0%
62 86
 
1.0%
61 241
2.7%
60 371
4.2%

customer_segment
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Premium
1881 
Business
1822 
Other
1806 
Student
1667 
Retail
1651 

Length

Max length8
Median length7
Mean length6.6101733
Min length5

Characters and Unicode

Total characters58348
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowPremium
3rd rowPremium
4th rowPremium
5th rowPremium

Common Values

ValueCountFrequency (%)
Premium 1881
21.3%
Business 1822
20.6%
Other 1806
20.5%
Student 1667
18.9%
Retail 1651
18.7%

Length

2023-09-09T06:20:49.437947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:20:50.077738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
premium 1881
21.3%
business 1822
20.6%
other 1806
20.5%
student 1667
18.9%
retail 1651
18.7%

Most occurring characters

ValueCountFrequency (%)
e 8827
15.1%
t 6791
11.6%
s 5466
9.4%
u 5370
9.2%
i 5354
9.2%
m 3762
 
6.4%
r 3687
 
6.3%
n 3489
 
6.0%
P 1881
 
3.2%
B 1822
 
3.1%
Other values (7) 11899
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49521
84.9%
Uppercase Letter 8827
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8827
17.8%
t 6791
13.7%
s 5466
11.0%
u 5370
10.8%
i 5354
10.8%
m 3762
7.6%
r 3687
7.4%
n 3489
 
7.0%
h 1806
 
3.6%
d 1667
 
3.4%
Other values (2) 3302
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
P 1881
21.3%
B 1822
20.6%
O 1806
20.5%
S 1667
18.9%
R 1651
18.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 58348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8827
15.1%
t 6791
11.6%
s 5466
9.4%
u 5370
9.2%
i 5354
9.2%
m 3762
 
6.4%
r 3687
 
6.3%
n 3489
 
6.0%
P 1881
 
3.2%
B 1822
 
3.1%
Other values (7) 11899
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8827
15.1%
t 6791
11.6%
s 5466
9.4%
u 5370
9.2%
i 5354
9.2%
m 3762
 
6.4%
r 3687
 
6.3%
n 3489
 
6.0%
P 1881
 
3.2%
B 1822
 
3.1%
Other values (7) 11899
20.4%

card_type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Other
1904 
Rupay
1852 
American Express
1730 
MasterCard
1684 
Visa
1657 

Length

Max length16
Median length10
Mean length7.9220573
Min length4

Characters and Unicode

Total characters69928
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Other 1904
21.6%
Rupay 1852
21.0%
American Express 1730
19.6%
MasterCard 1684
19.1%
Visa 1657
18.8%

Length

2023-09-09T06:20:51.258359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:20:51.885187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
other 1904
18.0%
rupay 1852
17.5%
american 1730
16.4%
express 1730
16.4%
mastercard 1684
16.0%
visa 1657
15.7%

Most occurring characters

ValueCountFrequency (%)
r 8732
 
12.5%
a 8607
 
12.3%
e 7048
 
10.1%
s 6801
 
9.7%
t 3588
 
5.1%
p 3582
 
5.1%
i 3387
 
4.8%
O 1904
 
2.7%
h 1904
 
2.7%
R 1852
 
2.6%
Other values (13) 22523
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55957
80.0%
Uppercase Letter 12241
 
17.5%
Space Separator 1730
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8732
15.6%
a 8607
15.4%
e 7048
12.6%
s 6801
12.2%
t 3588
6.4%
p 3582
6.4%
i 3387
 
6.1%
h 1904
 
3.4%
u 1852
 
3.3%
y 1852
 
3.3%
Other values (5) 8604
15.4%
Uppercase Letter
ValueCountFrequency (%)
O 1904
15.6%
R 1852
15.1%
E 1730
14.1%
A 1730
14.1%
M 1684
13.8%
C 1684
13.8%
V 1657
13.5%
Space Separator
ValueCountFrequency (%)
1730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68198
97.5%
Common 1730
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 8732
12.8%
a 8607
12.6%
e 7048
 
10.3%
s 6801
 
10.0%
t 3588
 
5.3%
p 3582
 
5.3%
i 3387
 
5.0%
O 1904
 
2.8%
h 1904
 
2.8%
R 1852
 
2.7%
Other values (12) 20793
30.5%
Common
ValueCountFrequency (%)
1730
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 8732
 
12.5%
a 8607
 
12.3%
e 7048
 
10.1%
s 6801
 
9.7%
t 3588
 
5.1%
p 3582
 
5.1%
i 3387
 
4.8%
O 1904
 
2.7%
h 1904
 
2.7%
R 1852
 
2.6%
Other values (13) 22523
32.2%

customer_location
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Pune
1110 
Jaipur
1012 
Mumbai
954 
Delhi
922 
Hyderabad
910 
Other values (5)
3919 

Length

Max length9
Median length7
Mean length6.7321853
Min length4

Characters and Unicode

Total characters59425
Distinct characters29
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata
2nd rowKolkata
3rd rowKolkata
4th rowKolkata
5th rowKolkata

Common Values

ValueCountFrequency (%)
Pune 1110
12.6%
Jaipur 1012
11.5%
Mumbai 954
10.8%
Delhi 922
10.4%
Hyderabad 910
10.3%
Kolkata 847
9.6%
Lucknow 804
9.1%
Chennai 790
8.9%
Bangalore 743
8.4%
Ahmedabad 735
8.3%

Length

2023-09-09T06:20:52.555946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:20:53.312705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pune 1110
12.6%
jaipur 1012
11.5%
mumbai 954
10.8%
delhi 922
10.4%
hyderabad 910
10.3%
kolkata 847
9.6%
lucknow 804
9.1%
chennai 790
8.9%
bangalore 743
8.4%
ahmedabad 735
8.3%

Most occurring characters

ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50598
85.1%
Uppercase Letter 8827
 
14.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9226
18.2%
e 5210
10.3%
n 4237
 
8.4%
u 3880
 
7.7%
i 3678
 
7.3%
d 3290
 
6.5%
r 2665
 
5.3%
b 2599
 
5.1%
l 2512
 
5.0%
h 2447
 
4.8%
Other values (9) 10854
21.5%
Uppercase Letter
ValueCountFrequency (%)
P 1110
12.6%
J 1012
11.5%
M 954
10.8%
D 922
10.4%
H 910
10.3%
K 847
9.6%
L 804
9.1%
C 790
8.9%
B 743
8.4%
A 735
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 59425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

merchant_name
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
airtel
664 
rakuten
660 
amazon
657 
amazon gift cards
636 
instamart
628 
Other values (13)
5582 

Length

Max length17
Median length15
Mean length11.171859
Min length6

Characters and Unicode

Total characters98614
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowairtel
2nd rowfake_merchant_2
3rd rowrakuten
4th rowrakuten
5th rowinstamart

Common Values

ValueCountFrequency (%)
airtel 664
 
7.5%
rakuten 660
 
7.5%
amazon 657
 
7.4%
amazon gift cards 636
 
7.2%
instamart 628
 
7.1%
zomato 625
 
7.1%
swiggy 614
 
7.0%
chai talks 595
 
6.7%
fake_merchant_5 394
 
4.5%
fake_merchant_6 393
 
4.5%
Other values (8) 2961
33.5%

Length

2023-09-09T06:20:54.327380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
amazon 1293
 
12.1%
airtel 664
 
6.2%
rakuten 660
 
6.2%
gift 636
 
5.9%
cards 636
 
5.9%
instamart 628
 
5.9%
zomato 625
 
5.8%
swiggy 614
 
5.7%
chai 595
 
5.6%
talks 595
 
5.6%
Other values (10) 3748
35.0%

Most occurring characters

ValueCountFrequency (%)
a 15113
15.3%
e 8820
 
8.9%
t 8184
 
8.3%
_ 7496
 
7.6%
r 6336
 
6.4%
n 6329
 
6.4%
m 6294
 
6.4%
k 5003
 
5.1%
c 4979
 
5.0%
f 4384
 
4.4%
Other values (22) 25676
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85503
86.7%
Connector Punctuation 7496
 
7.6%
Decimal Number 3748
 
3.8%
Space Separator 1867
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15113
17.7%
e 8820
10.3%
t 8184
9.6%
r 6336
7.4%
n 6329
7.4%
m 6294
7.4%
k 5003
 
5.9%
c 4979
 
5.8%
f 4384
 
5.1%
h 4343
 
5.1%
Other values (10) 15718
18.4%
Decimal Number
ValueCountFrequency (%)
5 394
10.5%
6 393
10.5%
9 392
10.5%
8 391
10.4%
7 384
10.2%
0 373
10.0%
3 364
9.7%
2 355
9.5%
1 354
9.4%
4 348
9.3%
Connector Punctuation
ValueCountFrequency (%)
_ 7496
100.0%
Space Separator
ValueCountFrequency (%)
1867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85503
86.7%
Common 13111
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15113
17.7%
e 8820
10.3%
t 8184
9.6%
r 6336
7.4%
n 6329
7.4%
m 6294
7.4%
k 5003
 
5.9%
c 4979
 
5.8%
f 4384
 
5.1%
h 4343
 
5.1%
Other values (10) 15718
18.4%
Common
ValueCountFrequency (%)
_ 7496
57.2%
1867
 
14.2%
5 394
 
3.0%
6 393
 
3.0%
9 392
 
3.0%
8 391
 
3.0%
7 384
 
2.9%
0 373
 
2.8%
3 364
 
2.8%
2 355
 
2.7%
Other values (2) 702
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15113
15.3%
e 8820
 
8.9%
t 8184
 
8.3%
_ 7496
 
7.6%
r 6336
 
6.4%
n 6329
 
6.4%
m 6294
 
6.4%
k 5003
 
5.1%
c 4979
 
5.0%
f 4384
 
4.4%
Other values (22) 25676
26.0%
Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Paris, France
 
495
Tokyo, Japan
 
483
Bangalore
 
480
Kolkata
 
465
Delhi
 
463
Other values (15)
6441 

Length

Max length23
Median length17
Mean length11.390506
Min length4

Characters and Unicode

Total characters100544
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangalore
2nd rowChennai
3rd rowSydney, Australia
4th rowPune
5th rowBangalore

Common Values

ValueCountFrequency (%)
Paris, France 495
 
5.6%
Tokyo, Japan 483
 
5.5%
Bangalore 480
 
5.4%
Kolkata 465
 
5.3%
Delhi 463
 
5.2%
Lucknow 453
 
5.1%
Pune 443
 
5.0%
Ahmedabad 439
 
5.0%
Singapore, Singapore 438
 
5.0%
London, UK 438
 
5.0%
Other values (10) 4230
47.9%

Length

2023-09-09T06:20:55.134122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
singapore 876
 
5.5%
paris 495
 
3.1%
france 495
 
3.1%
japan 483
 
3.1%
tokyo 483
 
3.1%
bangalore 480
 
3.0%
kolkata 465
 
2.9%
delhi 463
 
2.9%
lucknow 453
 
2.9%
pune 443
 
2.8%
Other values (25) 10666
67.5%

Most occurring characters

ValueCountFrequency (%)
a 11908
 
11.8%
o 7483
 
7.4%
n 7042
 
7.0%
6975
 
6.9%
e 6164
 
6.1%
i 6089
 
6.1%
r 5729
 
5.7%
, 4411
 
4.4%
d 3411
 
3.4%
u 3019
 
3.0%
Other values (32) 38313
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71604
71.2%
Uppercase Letter 17554
 
17.5%
Space Separator 6975
 
6.9%
Other Punctuation 4411
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11908
16.6%
o 7483
10.5%
n 7042
9.8%
e 6164
 
8.6%
i 6089
 
8.5%
r 5729
 
8.0%
d 3411
 
4.8%
u 3019
 
4.2%
l 2262
 
3.2%
p 2199
 
3.1%
Other values (12) 16298
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 2160
12.3%
A 2157
12.3%
C 1687
9.6%
J 1329
 
7.6%
T 1316
 
7.5%
U 1308
 
7.5%
P 938
 
5.3%
B 906
 
5.2%
K 903
 
5.1%
D 897
 
5.1%
Other values (8) 3953
22.5%
Space Separator
ValueCountFrequency (%)
6975
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89158
88.7%
Common 11386
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11908
 
13.4%
o 7483
 
8.4%
n 7042
 
7.9%
e 6164
 
6.9%
i 6089
 
6.8%
r 5729
 
6.4%
d 3411
 
3.8%
u 3019
 
3.4%
l 2262
 
2.5%
p 2199
 
2.5%
Other values (30) 33852
38.0%
Common
ValueCountFrequency (%)
6975
61.3%
, 4411
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11908
 
11.8%
o 7483
 
7.4%
n 7042
 
7.0%
6975
 
6.9%
e 6164
 
6.1%
i 6089
 
6.1%
r 5729
 
5.7%
, 4411
 
4.4%
d 3411
 
3.4%
u 3019
 
3.0%
Other values (32) 38313
38.1%

trans_id
Text

UNIQUE 

Distinct8827
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
2023-09-09T06:20:55.919872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters158886
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8827 ?
Unique (%)100.0%

Sample

1st rowPvGyYAXFN5hE5HUzHy
2nd rowwLovPGOet4ZLCiABkq
3rd rowPnXTjxdEbojF3nY9Mn
4th rowLu6gvl9kDyu5N6MXkJ
5th rowKEtEuTEBLBG53KLfF3
ValueCountFrequency (%)
pvgyyaxfn5he5huzhy 1
 
< 0.1%
i0zzpawzwh1ke8s5dr 1
 
< 0.1%
4cjbll6f3gatlfnrrp 1
 
< 0.1%
ytkrwvnsnqc1ksevnu 1
 
< 0.1%
jw8habwcbcctypuoth 1
 
< 0.1%
l0ucneohu50yzybzzh 1
 
< 0.1%
tawmwfo65ya6b3oxnc 1
 
< 0.1%
3lxth86xu1uwcvrd0e 1
 
< 0.1%
natou69ijuzbxzvte4 1
 
< 0.1%
3xzwihivrpsiyng3uz 1
 
< 0.1%
Other values (8817) 8817
99.9%
2023-09-09T06:20:57.367411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 2689
 
1.7%
2 2658
 
1.7%
X 2655
 
1.7%
i 2641
 
1.7%
b 2633
 
1.7%
g 2631
 
1.7%
k 2628
 
1.7%
0 2622
 
1.7%
a 2607
 
1.6%
f 2606
 
1.6%
Other values (52) 132516
83.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66601
41.9%
Lowercase Letter 66560
41.9%
Decimal Number 25725
 
16.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 2689
 
4.0%
X 2655
 
4.0%
J 2605
 
3.9%
G 2601
 
3.9%
L 2599
 
3.9%
H 2595
 
3.9%
W 2589
 
3.9%
V 2589
 
3.9%
U 2587
 
3.9%
T 2587
 
3.9%
Other values (16) 40505
60.8%
Lowercase Letter
ValueCountFrequency (%)
i 2641
 
4.0%
b 2633
 
4.0%
g 2631
 
4.0%
k 2628
 
3.9%
a 2607
 
3.9%
f 2606
 
3.9%
h 2598
 
3.9%
r 2591
 
3.9%
x 2576
 
3.9%
s 2570
 
3.9%
Other values (16) 40479
60.8%
Decimal Number
ValueCountFrequency (%)
2 2658
10.3%
0 2622
10.2%
3 2601
10.1%
5 2601
10.1%
4 2566
10.0%
8 2561
10.0%
6 2544
9.9%
1 2533
9.8%
9 2528
9.8%
7 2511
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 133161
83.8%
Common 25725
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 2689
 
2.0%
X 2655
 
2.0%
i 2641
 
2.0%
b 2633
 
2.0%
g 2631
 
2.0%
k 2628
 
2.0%
a 2607
 
2.0%
f 2606
 
2.0%
J 2605
 
2.0%
G 2601
 
2.0%
Other values (42) 106865
80.3%
Common
ValueCountFrequency (%)
2 2658
10.3%
0 2622
10.2%
3 2601
10.1%
5 2601
10.1%
4 2566
10.0%
8 2561
10.0%
6 2544
9.9%
1 2533
9.8%
9 2528
9.8%
7 2511
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 2689
 
1.7%
2 2658
 
1.7%
X 2655
 
1.7%
i 2641
 
1.7%
b 2633
 
1.7%
g 2631
 
1.7%
k 2628
 
1.7%
0 2622
 
1.7%
a 2607
 
1.6%
f 2606
 
1.6%
Other values (52) 132516
83.4%

trans_approval_code
Text

UNIQUE 

Distinct8827
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
2023-09-09T06:20:58.454062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters52962
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8827 ?
Unique (%)100.0%

Sample

1st row7473NB
2nd rowCFY0Y0
3rd rowNNEI38
4th rowG4TUYK
5th rowORLXBN
ValueCountFrequency (%)
7473nb 1
 
< 0.1%
inqlms 1
 
< 0.1%
6bjs77 1
 
< 0.1%
13xdhr 1
 
< 0.1%
5lccif 1
 
< 0.1%
h921k5 1
 
< 0.1%
q8pysb 1
 
< 0.1%
19f9qm 1
 
< 0.1%
w8tpxa 1
 
< 0.1%
5oaaf9 1
 
< 0.1%
Other values (8817) 8817
99.9%
2023-09-09T06:21:00.122532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 1549
 
2.9%
P 1548
 
2.9%
K 1527
 
2.9%
4 1513
 
2.9%
Q 1512
 
2.9%
U 1509
 
2.8%
F 1508
 
2.8%
H 1506
 
2.8%
V 1504
 
2.8%
Z 1496
 
2.8%
Other values (26) 37790
71.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38275
72.3%
Decimal Number 14687
 
27.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1548
 
4.0%
K 1527
 
4.0%
Q 1512
 
4.0%
U 1509
 
3.9%
F 1508
 
3.9%
H 1506
 
3.9%
V 1504
 
3.9%
Z 1496
 
3.9%
T 1492
 
3.9%
Y 1487
 
3.9%
Other values (16) 23186
60.6%
Decimal Number
ValueCountFrequency (%)
8 1549
10.5%
4 1513
10.3%
7 1494
10.2%
0 1492
10.2%
3 1455
9.9%
9 1451
9.9%
6 1447
9.9%
2 1440
9.8%
1 1434
9.8%
5 1412
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 38275
72.3%
Common 14687
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1548
 
4.0%
K 1527
 
4.0%
Q 1512
 
4.0%
U 1509
 
3.9%
F 1508
 
3.9%
H 1506
 
3.9%
V 1504
 
3.9%
Z 1496
 
3.9%
T 1492
 
3.9%
Y 1487
 
3.9%
Other values (16) 23186
60.6%
Common
ValueCountFrequency (%)
8 1549
10.5%
4 1513
10.3%
7 1494
10.2%
0 1492
10.2%
3 1455
9.9%
9 1451
9.9%
6 1447
9.9%
2 1440
9.8%
1 1434
9.8%
5 1412
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1549
 
2.9%
P 1548
 
2.9%
K 1527
 
2.9%
4 1513
 
2.9%
Q 1512
 
2.9%
U 1509
 
2.8%
F 1508
 
2.8%
H 1506
 
2.8%
V 1504
 
2.8%
Z 1496
 
2.8%
Other values (26) 37790
71.4%

trans_loc
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Pune
1110 
Jaipur
1012 
Mumbai
954 
Delhi
922 
Hyderabad
910 
Other values (5)
3919 

Length

Max length9
Median length7
Mean length6.7321853
Min length4

Characters and Unicode

Total characters59425
Distinct characters29
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata
2nd rowKolkata
3rd rowKolkata
4th rowKolkata
5th rowKolkata

Common Values

ValueCountFrequency (%)
Pune 1110
12.6%
Jaipur 1012
11.5%
Mumbai 954
10.8%
Delhi 922
10.4%
Hyderabad 910
10.3%
Kolkata 847
9.6%
Lucknow 804
9.1%
Chennai 790
8.9%
Bangalore 743
8.4%
Ahmedabad 735
8.3%

Length

2023-09-09T06:21:00.831300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:01.542073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pune 1110
12.6%
jaipur 1012
11.5%
mumbai 954
10.8%
delhi 922
10.4%
hyderabad 910
10.3%
kolkata 847
9.6%
lucknow 804
9.1%
chennai 790
8.9%
bangalore 743
8.4%
ahmedabad 735
8.3%

Most occurring characters

ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50598
85.1%
Uppercase Letter 8827
 
14.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9226
18.2%
e 5210
10.3%
n 4237
 
8.4%
u 3880
 
7.7%
i 3678
 
7.3%
d 3290
 
6.5%
r 2665
 
5.3%
b 2599
 
5.1%
l 2512
 
5.0%
h 2447
 
4.8%
Other values (9) 10854
21.5%
Uppercase Letter
ValueCountFrequency (%)
P 1110
12.6%
J 1012
11.5%
M 954
10.8%
D 922
10.4%
H 910
10.3%
K 847
9.6%
L 804
9.1%
C 790
8.9%
B 743
8.4%
A 735
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 59425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9226
15.5%
e 5210
 
8.8%
n 4237
 
7.1%
u 3880
 
6.5%
i 3678
 
6.2%
d 3290
 
5.5%
r 2665
 
4.5%
b 2599
 
4.4%
l 2512
 
4.2%
h 2447
 
4.1%
Other values (19) 19681
33.1%

trans_cat
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Entertainment
1302 
Travel
1291 
Utilities
1260 
Retail
1246 
Grocery
1245 
Other values (2)
2483 

Length

Max length13
Median length9
Mean length7.4611986
Min length5

Characters and Unicode

Total characters65860
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRetail
2nd rowUtilities
3rd rowUtilities
4th rowRetail
5th rowDining

Common Values

ValueCountFrequency (%)
Entertainment 1302
14.8%
Travel 1291
14.6%
Utilities 1260
14.3%
Retail 1246
14.1%
Grocery 1245
14.1%
Dining 1242
14.1%
Other 1241
14.1%

Length

2023-09-09T06:21:02.369808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:03.060588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
entertainment 1302
14.8%
travel 1291
14.6%
utilities 1260
14.3%
retail 1246
14.1%
grocery 1245
14.1%
dining 1242
14.1%
other 1241
14.1%

Most occurring characters

ValueCountFrequency (%)
t 8913
13.5%
e 8887
13.5%
i 8812
13.4%
n 6390
9.7%
r 6324
9.6%
a 3839
 
5.8%
l 3797
 
5.8%
E 1302
 
2.0%
m 1302
 
2.0%
T 1291
 
2.0%
Other values (12) 15003
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57033
86.6%
Uppercase Letter 8827
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 8913
15.6%
e 8887
15.6%
i 8812
15.5%
n 6390
11.2%
r 6324
11.1%
a 3839
6.7%
l 3797
6.7%
m 1302
 
2.3%
v 1291
 
2.3%
s 1260
 
2.2%
Other values (5) 6218
10.9%
Uppercase Letter
ValueCountFrequency (%)
E 1302
14.8%
T 1291
14.6%
U 1260
14.3%
R 1246
14.1%
G 1245
14.1%
D 1242
14.1%
O 1241
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 65860
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8913
13.5%
e 8887
13.5%
i 8812
13.4%
n 6390
9.7%
r 6324
9.6%
a 3839
 
5.8%
l 3797
 
5.8%
E 1302
 
2.0%
m 1302
 
2.0%
T 1291
 
2.0%
Other values (12) 15003
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 8913
13.5%
e 8887
13.5%
i 8812
13.4%
n 6390
9.7%
r 6324
9.6%
a 3839
 
5.8%
l 3797
 
5.8%
E 1302
 
2.0%
m 1302
 
2.0%
T 1291
 
2.0%
Other values (12) 15003
22.8%

trans_currency
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
INR
5072 
CAD
648 
JPY
643 
USD
630 
AUD
629 
Other values (2)
1205 

Length

Max length5
Median length3
Mean length3.1411578
Min length3

Characters and Unicode

Total characters27727
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINR
2nd rowINR
3rd rowAUD
4th rowAUD
5th rowINR

Common Values

ValueCountFrequency (%)
INR 5072
57.5%
CAD 648
 
7.3%
JPY 643
 
7.3%
USD 630
 
7.1%
AUD 629
 
7.1%
Other 623
 
7.1%
EUR 582
 
6.6%

Length

2023-09-09T06:21:03.887329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:04.630880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
inr 5072
57.5%
cad 648
 
7.3%
jpy 643
 
7.3%
usd 630
 
7.1%
aud 629
 
7.1%
other 623
 
7.1%
eur 582
 
6.6%

Most occurring characters

ValueCountFrequency (%)
R 5654
20.4%
I 5072
18.3%
N 5072
18.3%
D 1907
 
6.9%
U 1841
 
6.6%
A 1277
 
4.6%
C 648
 
2.3%
Y 643
 
2.3%
P 643
 
2.3%
J 643
 
2.3%
Other values (7) 4327
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25235
91.0%
Lowercase Letter 2492
 
9.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 5654
22.4%
I 5072
20.1%
N 5072
20.1%
D 1907
 
7.6%
U 1841
 
7.3%
A 1277
 
5.1%
C 648
 
2.6%
Y 643
 
2.5%
P 643
 
2.5%
J 643
 
2.5%
Other values (3) 1835
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
t 623
25.0%
h 623
25.0%
e 623
25.0%
r 623
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27727
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 5654
20.4%
I 5072
18.3%
N 5072
18.3%
D 1907
 
6.9%
U 1841
 
6.6%
A 1277
 
4.6%
C 648
 
2.3%
Y 643
 
2.3%
P 643
 
2.3%
J 643
 
2.3%
Other values (7) 4327
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 5654
20.4%
I 5072
18.3%
N 5072
18.3%
D 1907
 
6.9%
U 1841
 
6.6%
A 1277
 
4.6%
C 648
 
2.3%
Y 643
 
2.3%
P 643
 
2.3%
J 643
 
2.3%
Other values (7) 4327
15.6%

mcc
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4008.33
Minimum4000
Maximum4019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.1 KiB
2023-09-09T06:21:05.050692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile4001
Q14003
median4008
Q34013
95-th percentile4017
Maximum4019
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4188039
Coefficient of variation (CV)0.0013518857
Kurtosis-1.1792821
Mean4008.33
Median Absolute Deviation (MAD)5
Skewness0.2204128
Sum35381529
Variance29.363435
MonotonicityNot monotonic
2023-09-09T06:21:05.512044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4005 832
 
9.4%
4003 749
 
8.5%
4001 734
 
8.3%
4006 696
 
7.9%
4014 680
 
7.7%
4016 609
 
6.9%
4009 597
 
6.8%
4002 542
 
6.1%
4010 522
 
5.9%
4012 420
 
4.8%
Other values (10) 2446
27.7%
ValueCountFrequency (%)
4000 272
 
3.1%
4001 734
8.3%
4002 542
6.1%
4003 749
8.5%
4004 246
 
2.8%
4005 832
9.4%
4006 696
7.9%
4007 139
 
1.6%
4008 401
4.5%
4009 597
6.8%
ValueCountFrequency (%)
4019 156
 
1.8%
4018 188
 
2.1%
4017 291
3.3%
4016 609
6.9%
4015 172
 
1.9%
4014 680
7.7%
4013 400
4.5%
4012 420
4.8%
4011 181
 
2.1%
4010 522
5.9%

trans_amount
Real number (ℝ)

Distinct3502
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1028.0971
Minimum1.04
Maximum49890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.1 KiB
2023-09-09T06:21:05.982809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile4
Q114.54
median28
Q341.730001
95-th percentile2719.105
Maximum49890
Range49888.96
Interquartile range (IQR)27.190001

Descriptive statistics

Standard deviation5431.5416
Coefficient of variation (CV)5.2831016
Kurtosis44.3273
Mean1028.0971
Median Absolute Deviation (MAD)13.6
Skewness6.5483891
Sum9075013.5
Variance29501645
MonotonicityNot monotonic
2023-09-09T06:21:06.516013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 100
 
1.1%
13 99
 
1.1%
39 98
 
1.1%
23 97
 
1.1%
6 96
 
1.1%
8 94
 
1.1%
42 93
 
1.1%
47 93
 
1.1%
41 93
 
1.1%
34 92
 
1.0%
Other values (3492) 7872
89.2%
ValueCountFrequency (%)
1.039999962 1
< 0.1%
1.049999952 1
< 0.1%
1.070000052 1
< 0.1%
1.080000043 1
< 0.1%
1.090000033 1
< 0.1%
1.100000024 1
< 0.1%
1.120000005 2
< 0.1%
1.129999995 1
< 0.1%
1.159999967 1
< 0.1%
1.169999957 1
< 0.1%
ValueCountFrequency (%)
49890 1
< 0.1%
49592 1
< 0.1%
49246 1
< 0.1%
49087.94141 1
< 0.1%
49009.78125 1
< 0.1%
48734 1
< 0.1%
48496.19141 1
< 0.1%
48451.64062 1
< 0.1%
48443.44922 1
< 0.1%
48371 1
< 0.1%
Distinct8816
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Minimum2023-08-10 06:34:19
Maximum2023-09-09 06:16:29
2023-09-09T06:21:07.002320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:21:07.504282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Debit Card
4462 
Credit Card
4365 

Length

Max length11
Median length10
Mean length10.494505
Min length10

Characters and Unicode

Total characters92635
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowCredit Card
3rd rowDebit Card
4th rowDebit Card
5th rowDebit Card

Common Values

ValueCountFrequency (%)
Debit Card 4462
50.5%
Credit Card 4365
49.5%

Length

2023-09-09T06:21:07.942855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:08.317847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
card 8827
50.0%
debit 4462
25.3%
credit 4365
24.7%

Most occurring characters

ValueCountFrequency (%)
C 13192
14.2%
r 13192
14.2%
d 13192
14.2%
e 8827
9.5%
i 8827
9.5%
t 8827
9.5%
8827
9.5%
a 8827
9.5%
D 4462
 
4.8%
b 4462
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66154
71.4%
Uppercase Letter 17654
 
19.1%
Space Separator 8827
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 13192
19.9%
d 13192
19.9%
e 8827
13.3%
i 8827
13.3%
t 8827
13.3%
a 8827
13.3%
b 4462
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 13192
74.7%
D 4462
 
25.3%
Space Separator
ValueCountFrequency (%)
8827
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83808
90.5%
Common 8827
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 13192
15.7%
r 13192
15.7%
d 13192
15.7%
e 8827
10.5%
i 8827
10.5%
t 8827
10.5%
a 8827
10.5%
D 4462
 
5.3%
b 4462
 
5.3%
Common
ValueCountFrequency (%)
8827
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 13192
14.2%
r 13192
14.2%
d 13192
14.2%
e 8827
9.5%
i 8827
9.5%
t 8827
9.5%
8827
9.5%
a 8827
9.5%
D 4462
 
4.8%
b 4462
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
PIN
4444 
Biometric
4383 

Length

Max length9
Median length3
Mean length5.9792682
Min length3

Characters and Unicode

Total characters52779
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPIN
2nd rowBiometric
3rd rowPIN
4th rowBiometric
5th rowBiometric

Common Values

ValueCountFrequency (%)
PIN 4444
50.3%
Biometric 4383
49.7%

Length

2023-09-09T06:21:08.632258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:09.024862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pin 4444
50.3%
biometric 4383
49.7%

Most occurring characters

ValueCountFrequency (%)
i 8766
16.6%
P 4444
8.4%
I 4444
8.4%
N 4444
8.4%
B 4383
8.3%
o 4383
8.3%
m 4383
8.3%
e 4383
8.3%
t 4383
8.3%
r 4383
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35064
66.4%
Uppercase Letter 17715
33.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8766
25.0%
o 4383
12.5%
m 4383
12.5%
e 4383
12.5%
t 4383
12.5%
r 4383
12.5%
c 4383
12.5%
Uppercase Letter
ValueCountFrequency (%)
P 4444
25.1%
I 4444
25.1%
N 4444
25.1%
B 4383
24.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 52779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8766
16.6%
P 4444
8.4%
I 4444
8.4%
N 4444
8.4%
B 4383
8.3%
o 4383
8.3%
m 4383
8.3%
e 4383
8.3%
t 4383
8.3%
r 4383
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 8766
16.6%
P 4444
8.4%
I 4444
8.4%
N 4444
8.4%
B 4383
8.3%
o 4383
8.3%
m 4383
8.3%
e 4383
8.3%
t 4383
8.3%
r 4383
8.3%

trans_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
Payment
2963 
Purchase
2953 
Transfer
2911 

Length

Max length8
Median length8
Mean length7.6643254
Min length7

Characters and Unicode

Total characters67653
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransfer
2nd rowTransfer
3rd rowPayment
4th rowPurchase
5th rowTransfer

Common Values

ValueCountFrequency (%)
Payment 2963
33.6%
Purchase 2953
33.5%
Transfer 2911
33.0%

Length

2023-09-09T06:21:09.352980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-09T06:21:09.714167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
payment 2963
33.6%
purchase 2953
33.5%
transfer 2911
33.0%

Most occurring characters

ValueCountFrequency (%)
a 8827
13.0%
e 8827
13.0%
r 8775
13.0%
P 5916
8.7%
n 5874
8.7%
s 5864
8.7%
y 2963
 
4.4%
m 2963
 
4.4%
t 2963
 
4.4%
u 2953
 
4.4%
Other values (4) 11728
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58826
87.0%
Uppercase Letter 8827
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8827
15.0%
e 8827
15.0%
r 8775
14.9%
n 5874
10.0%
s 5864
10.0%
y 2963
 
5.0%
m 2963
 
5.0%
t 2963
 
5.0%
u 2953
 
5.0%
c 2953
 
5.0%
Other values (2) 5864
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 5916
67.0%
T 2911
33.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67653
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8827
13.0%
e 8827
13.0%
r 8775
13.0%
P 5916
8.7%
n 5874
8.7%
s 5864
8.7%
y 2963
 
4.4%
m 2963
 
4.4%
t 2963
 
4.4%
u 2953
 
4.4%
Other values (4) 11728
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8827
13.0%
e 8827
13.0%
r 8775
13.0%
P 5916
8.7%
n 5874
8.7%
s 5864
8.7%
y 2963
 
4.4%
m 2963
 
4.4%
t 2963
 
4.4%
u 2953
 
4.4%
Other values (4) 11728
17.3%

fake
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
True
7581 
False
1246 
ValueCountFrequency (%)
True 7581
85.9%
False 1246
 
14.1%
2023-09-09T06:21:10.106862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-09-09T06:20:29.926254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:19:43.240986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:13.281132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:20.904480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:36.480622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:19:57.145251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:20.000535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:28.966583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:36.768894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:02.504831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:20.280573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:29.261462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:37.160383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:08.303620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:20.603595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-09T06:20:29.576599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-09T06:21:10.419361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
card_numbercustomer_agemcctrans_amountcustomer_segmentcard_typecustomer_locationmerchant_namemerchant_locationtrans_loctrans_cattrans_currencytrans_payment_methodtrans_verify_methodtrans_statusfake
card_number1.000-0.0010.0070.0020.9720.9720.9720.0000.0420.9720.0000.0000.0000.0000.0620.000
customer_age-0.0011.0000.0150.0030.1350.1310.1380.0110.0160.1380.0180.0070.0240.0180.0000.000
mcc0.0070.0151.0000.0150.0000.0090.0000.0120.0000.0000.0000.0200.0000.0210.0250.024
trans_amount0.0020.0030.0151.0000.0150.0280.0000.0480.0140.0000.0000.0550.0150.0070.0000.445
customer_segment0.9720.1350.0000.0151.0000.0800.1250.0120.0000.1250.0000.0130.0000.0050.0000.000
card_type0.9720.1310.0090.0280.0801.0000.1250.0170.0190.1250.0070.0160.0170.0070.0000.000
customer_location0.9720.1380.0000.0000.1250.1251.0000.0000.0001.0000.0010.0000.0070.0200.0000.000
merchant_name0.0000.0110.0120.0480.0120.0170.0001.0000.0000.0000.0140.0530.0000.0180.0000.347
merchant_location0.0420.0160.0000.0140.0000.0190.0000.0001.0000.0000.0000.0160.0000.0000.0000.021
trans_loc0.9720.1380.0000.0000.1250.1251.0000.0000.0001.0000.0010.0000.0070.0200.0000.000
trans_cat0.0000.0180.0000.0000.0000.0070.0010.0140.0000.0011.0000.0000.0000.0000.0000.016
trans_currency0.0000.0070.0200.0550.0130.0160.0000.0530.0160.0000.0001.0000.0060.0000.0000.348
trans_payment_method0.0000.0240.0000.0150.0000.0170.0070.0000.0000.0070.0000.0061.0000.0000.0110.000
trans_verify_method0.0000.0180.0210.0070.0050.0070.0200.0180.0000.0200.0000.0000.0001.0000.0190.000
trans_status0.0620.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0191.0000.000
fake0.0000.0000.0240.4450.0000.0000.0000.3470.0210.0000.0160.3480.0000.0000.0001.000

Missing values

2023-09-09T06:20:38.341490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-09T06:20:40.278872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cardholder_namecard_numbercustomer_agecustomer_segmentcard_typecustomer_locationmerchant_namemerchant_locationtrans_idtrans_approval_codetrans_loctrans_cattrans_currencymcctrans_amounttrans_datetrans_payment_methodtrans_verify_methodtrans_statusfake
0Andrea Hess53617481446561PremiumOtherKolkataairtelBangalorePvGyYAXFN5hE5HUzHy7473NBKolkataRetailINR40021321.3399662023-09-06 17:19:12Debit CardPINTransferFalse
1Andrea Hess53617481446561PremiumOtherKolkatafake_merchant_2ChennaiwLovPGOet4ZLCiABkqCFY0Y0KolkataUtilitiesINR400319.3400002023-09-06 17:19:48Credit CardBiometricTransferTrue
2Andrea Hess53617481446561PremiumOtherKolkatarakutenSydney, AustraliaPnXTjxdEbojF3nY9MnNNEI38KolkataUtilitiesAUD400130.7300002023-09-06 17:20:33Debit CardPINPaymentTrue
3Andrea Hess53617481446561PremiumOtherKolkatarakutenPuneLu6gvl9kDyu5N6MXkJG4TUYKKolkataRetailAUD401115.0000002023-09-06 17:21:03Debit CardBiometricPurchaseTrue
4Andrea Hess53617481446561PremiumOtherKolkatainstamartBangaloreKEtEuTEBLBG53KLfF3ORLXBNKolkataDiningINR401949.5999982023-09-06 17:21:47Debit CardBiometricTransferTrue
5Andrea Hess53617481446561PremiumOtherKolkatafake_merchant_8Lucknowk9bTI7xbFA2EOKWO5aM8RWCEKolkataGroceryINR401647.8200002023-09-06 17:22:25Credit CardBiometricPaymentTrue
6Andrea Hess53617481446561PremiumOtherKolkatafake_merchant_9Rio de Janeiro, Brazil7FUAuqhisZeXyx4pPCLIJU8KKolkataUtilitiesINR400142.3400002023-09-06 17:23:14Credit CardPINTransferTrue
7Andrea Hess53617481446561PremiumOtherKolkataairtelKolkatai4wHRAbWOLSh80Nc0pABVYXQKolkataGroceryINR401639.5099982023-09-06 17:23:39Debit CardPINTransferTrue
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